Top

Published in:

2018 | OriginalPaper | Chapter

Some Properties of Consensus-Based Classification

Authors : Vitaliy Tayanov, Adam Krzyżak, Ching Suen

Published in: Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The objective of this paper is to consider some properties of decisions produced by classifiers that are in consensus. Consensus allows strong classifiers to obtain very reliable classification on the objects on which consensus has been reached. For those ones where consensus is not reached the reclassification procedure should be applied based on other classification algorithms. Properties of different consensuses are described using algebraic approach and performance evaluation routine.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Recent Advances in Image Pre-processing Methods for Palmprint Biometrics

next chapter Knowledge Based Active Partition Approach for Heart Ventricle Recognition

Basu, M., Ho, T.K.: Data Complexity in Pattern Recognition. Springer, London (2006)CrossRefMATH

Biau, G., Devroye, L.: Lectures on the Nearest Neighbor Method. Springer, Cham (2015)CrossRefMATH

Frank, A., Asuncion, A.: Uci repository of machine learning databases. Technical report, School of Information and Computer Sciences, University of California, Irvine, CA (2010)

Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995). doi:10.1007/3-540-59119-2_166 CrossRef

Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. Springer, New York (2009)CrossRefMATH

Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 289–300 (2002)CrossRef

Kapustii, B., Rusyn, B., Tayanov, V.: Classifier optimization in small sample size condition. Autom. Control Comput. Sci. 40(5), 17–22 (2006)

Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)MATH

Kuncheva, L.: Combining Pattern Classifiers. Wiley, Hoboken (2014)CrossRefMATH

10.

Kyrgyzov, I.O., Maitre, H., Campedel, M.: A method of clustering combination applied to satellite image analysis. In: 14th International IEEE Conference on Image Analysis and Processing, pp. 81–86 (2007)

11.

Rokach, L.: Pattern Classification Using Ensemble Methods. World Scientific, Singapore (2009)CrossRefMATH

12.

Rusyn, B., Tayanov, V., Lutsyka, O.: Upper-bound estimates for classifiers based on a dissimilarity function. Cybern. Syst. Anal. 48(4), 592–600 (2012)CrossRefMATH

13.

Vapnik, V., Izmailov, R.: Statistical inference problems and their rigorous solutions. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds.) SLDS 2015. LNCS, vol. 9047, pp. 33–71. Springer, Cham (2015). doi:10.1007/978-3-319-17091-6_2 CrossRef

14.

Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)MATH

15.

Vorontsov, K.V.: Splitting and similarity phenomena in the sets of classifiers and their effect on the probability of overfitting. Pattern Recogn. Image Anal. 19(3), 412–420 (2009)MathSciNetCrossRef

16.

Vorontsov, K.: Combinatorial probability and the tightness of generalization bounds. Pattern Recogn. Image Anal. 18(2), 243–259 (2008)CrossRef

17.

Wozniak, M.: Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination. Springer, Heidelberg (2013)

18.

Xu, L., Krzyżak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3), 418–435 (1992)CrossRef

19.

Zhuravlev, Y.I.: On an algebraic approach to solving recognition or classification problems. Prob. Cybern. 33, 5–68 (1978). (in Russian)

Title: Some Properties of Consensus-Based Classification
Authors: Vitaliy Tayanov
Adam Krzyżak
Ching Suen
Publisher: Springer International Publishing
Book: Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017
Print ISBN: 978-3-319-59161-2

Electronic ISBN: 978-3-319-59162-9

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-59162-9_29

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner